Personal communication device as a hearing aid with real-time interactive user interface

ABSTRACT

Hearing aids for persons with sensorineural hearing loss aim to compensate for degraded speech perception caused by frequency-dependent elevation of hearing thresholds, reduced dynamic range, abnormal loudness growth, and increased temporal and spectral masking. A digital hearing aid is implemented as a smartphone application as an alternative to ASIC-based hearing aids. The implementation provides user-configurable processing for background noise suppression and dynamic range compression. Both processing blocks are implemented for real-time processing using single FFT-based analysis-synthesis. A touch-controlled graphical user interface enables the user to set and fine-tune the processing parameters in an interactive and real-time mode.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is the U.S. National Phase Application of InternationalApplication No. PCT/IN2019/050630, filed on Aug. 30, 2019, and assertspriority to Application No. IN 201821032763 filed Aug. 31, 2018, thedisclosures of which are hereby incorporated by reference in theirentirety.

TECHNICAL FIELD

The present disclosure relates to the field of signal processing foraudio systems, and more specifically relates to a personal communicationdevice as a hearing aid with a real-time interactive user interface forsetting the processing parameters.

BACKGROUND

Sensorineural hearing loss is associated with loss of sensory hair cellsin the cochlea or degeneration of the auditory nerve. It may beinherited genetically or may be caused by excessive noise exposure,aging, infection, or ototoxic drugs. It is characterized byfrequency-dependent elevation of hearing thresholds, abnormal growth ofloudness known as loudness recruitment, increased temporal and spectralmasking, and widening of auditory filters leading to degraded speechperception. Several signal-processing techniques have been reported forimproving the speech perception by patients suffering from sensorineuralhearing loss.

Frequency-selective amplification and dynamic range compression are theprimary processing techniques used in hearing aids (H. Dillon, HearingAids. New York: Thieme Medical, 2001; R. E. Sandlin, Textbook of HearingAid Amplification. San Diego, Cal.: Singular, 2000, pp. 210-220; D.Byrne, W. Tonnison, “Selecting the gain of hearing aids for persons withsensorineural hearing impairments,” Scandinavian Audiology, vol. 5, pp.51-59, 1976). Single-band dynamic range compression leads to reducedhigh-frequency audibility and multiband dynamic range compression maylead to perceptible distortion due to a transition of speech formantsacross the band boundaries. These problems can be addressed by usingsliding-band dynamic range compression (P. C. Pandey and N. Tiwari,“Dynamic range compression with low distortion for use in hearing aidsand audio systems,” U.S. Pat. No. 9,672,834, 2017). The compressionparameters can be tuned to fit the frequency-dependent thresholds andloudness recruitment of the patient.

Persons with sensorineural loss experience difficulty in understandingspeech in a noisy environment. Processing for noise suppression in ahearing aid can improve speech audibility and quality. Spectralsubtraction (S. F. Boll, “Suppression of acoustic noise in speech usingspectral subtraction,” IEEE Transactions on Acoustics, Speech and SignalProcessing, vol. 27, no. 2, pp. 113-120, 1979), a single-channel speechenhancement technique using an estimate of the noise spectrum, issuitable for such applications as it has low algorithmic delay andcomputational complexity. Dynamic quantile tracking based noiseestimation (P. C. Pandey and N. Tiwari, “Method and system forsuppressing noise in speech signals in hearing aids and speechcommunication devices,” U.S. Pat. No. 10,032,462B2, 2018) has beenproposed for tracking stationary and non-stationary noise efficientlyand it can be used for real-time noise suppression.

Hearing aids are designed using ASICs (application-specific integratedcircuits) due to power and size constraints. Therefore, incorporation ofa new compression technique in hearing aids and its field evaluation isprohibitively expensive. Use of smartphone-based application software(app) to customize and remotely configure settings on hearing aidsprovide greater flexibility to hearing aid users and developers. Manyhearing aid manufacturers (GN ReSound, Phonak, Unitron, Siemens, etc)provide apps to control hearing aids using Android or iOS smartphone.This type of app helps the hearing aid user in personalizing thelistening experience by adjustment of settings during use of the deviceand avoids repeated visits to an audiology clinic. The smartphone-basedapps may also be used for development and testing of signal processingtechniques for hearing aids. Hearing aid apps (e.g. ‘Petralex’,‘uSound’, ‘Q+’, and ‘BioAid’ for Android/iOS, ‘Mimi’, ‘EnhancedEars’ foriOS, and “Hearing Aid with Replay” and “Ear Assist” for Android) provideusers with moderate sensorineural hearing loss a low-cost alternativefor hearing aids. In addition to providing frequency-selective gain andmultiband dynamic range compression, they also offer the flexibility ofcreating and storing sound profiles specific to the user's hearing losscharacteristics. However, they do not allow the users to set theprocessing parameters in an interactive and real-time mode.

Ambrose et al. (S. D. Ambrose, S. P. Gido, and R. B. Schulein, “Hearingdevice system and method,” US Patent Application Publication No. US2012/0057734 A1, 2012) have described an in-ear audio coupling devicethat can be used with an audio signal device like a smartphone toperform the function of a hearing aid. The speech input from themicrophone of the smartphone is processed by the processor of thesmartphone and the processed output is given to the in-ear audiocoupling to serve as a hearing aid, and the software application on thesmartphone allows setting of the hearing loss profile. Neumann et al.(J. Neumann, N. Wack, N. M. Rodriguez, N. S. Grange, and J. Kinsbergen,“Consumer electronics device adapted for hearing loss compensation,” USPatent Application Publication No. US 2015/0195661A1, 2015) havedescribed a device with two software modules for outputting a hearingloss compensated signal. The first module either routes the audio signalto the output of the device for normal hearing listeners or routes theaudio signal to the input of the second software module. The secondmodule processes the audio signal for hearing loss compensation. Theprocessing parameters are input to the second module through a graphicaluser interface or a server connected through the internet.

Rader et al. (R. S. Rader, C. Menzel, B. W. Edwards, S. Puria, and B. B.Johansen, “Sound enhancement for mobile phones and others productsproducing personalized audio for users,” U.S. Pat. No. 7,529,545B2,2009) have described a personal communication device comprising atransmitter/receiver coupled to a communication medium for transmittingand receiving audio signals, control circuitry that controls thetransmission and reception and processing of call and audio signals, aspeaker, and a microphone. The control circuitry uses the preferredhearing profile of the user for processing the audio signals. Thehearing profile may be obtained from a remote server or through the userinterface of the device. The device also has a provision for hearingtest. Lang et al. (H. Lang, S. Jaaskclaincn, S. Karjalainen, O.Aaltoncn, T. Kaikuranta, P. Vuori, “Mobile station with audio signaladaptation to hearing characteristics of the user,” U.S. Pat. No.6,813,490B1, 2004) have described a method and apparatus for increasingthe intelligibility of speech signals in mobile communication, whereinthe acoustic parameters of the speech are modified in the frequencydomain, retaining the relative separation of the formants, to conform tothe listener's hearing profile, which may be selected from a menu ofpredetermined profiles or may be entered through the user interface. Theprocessing may be carried out on the communication network and thesignal routed to the target mobile device.

Camp (W. O. Camp Jr., “Mobile terminals including compensation forhearing impairment and methods and computer program products foroperating the same,” U.S. Pat. No. 7,613,314B2, 2009) has described adevice with a processor with a software for conducting a hearing test todetermine hearing profile of the listener, process the audio signals inaccordance with the hearing profile, and output the processed signalsthrough an earphone. Mouline (A. Mouline, “Adaptation of audio datafiles based on personal hearing profiles,” US Patent ApplicationPublication No. US 2002/0068986A1, 2002) has described a method andsystem for processing the audio to compensate for frequency-dependenthearing loss, with a facility for storing the hearing loss profiles.

Foo and Hughes (E. W. Foo and G. F. Hughes, “Remotely updating a hearingand profile,” U.S. Pat. No. 9,613,028B2, 2017) have described a methodfor updating a hearing loss profile stored in a hearing aid through adata link between the hearing aid and a hearing aid profile service.Westermann et al. (S. E. Westermann, S. V. Andersen, A. Westergaard, andN. E. B. Maretti, “System and method for managing a customizableconfiguration in a hearing aid,” International Publication No. WO2017/071757 A1, 2017) have described a system for managing hearing aidwith the hearing loss profile set through the internet. Westergaard andMaretti (A. Westergaard and N. E. B. Maretti, “System and method forpersonalizing a hearing aid,” International Publication No. WO2017/028876, 2017) have described a method of personalizing a hearingaid by setting the processing parameters in accordance with theaudiogram input from a server and further fine-tuning by an audiologist.

Thus, several devices have been reported for realizing hearing aids tocompensate for frequency-dependent hearing profile of the listener.However, these devices do not provide real-time suppression of thenonstationary background noise, which may severely degrade the speechperception by listeners with sensorineural hearing impairment. Further,the available devices do not permit setting of the processing parametersby the listener in an interactive mode to compensate for theindividual's abnormal frequency-dependent loudness growth curve. Thereis, therefore, a need to mitigate the disadvantages associated with theexisting devices, by devising a hearing aid with processing forsuppressing the background noise and a real-time interactive userinterface for setting the processing parameters.

SUMMARY

In an implementation of the present disclosure, a personal communicationdevice, such as a smartphone with an operating system and at least oneapplication for processing the stream of audio signals, may beconfigured to perform as a hearing aid. The application embedded intothe personal communication device provides signal processing for noisesuppression to improve speech quality and intelligibility forhearing-impaired listeners and dynamic range compression to compensatefor the individual user's frequency-dependent hearing loss and reduceddynamic range.

In another implementation, a method is disclosed for efficientimplementation of the processing by sharing the computation-intensiveoperations of the analysis-synthesis for the two types of processing,data buffering for reducing the input-output latency in real-timeprocessing, and interactive and real-time user interface for setting theprocessing parameters.

BRIEF DESCRIPTION OF THE DRAWINGS

The detailed description of the invention is described with reference tothe accompanying figures.

FIG. 1 is a schematic illustration of speech enhancement by spectralsubtraction, in accordance with an aspect of the present disclosure.

FIG. 2 is a schematic illustration of the dynamic quantile trackingtechnique used for tracking quantiles for estimation of the noisespectrum, in accordance with an aspect of the present disclosure.

FIG. 3 is a schematic illustration of sliding-band compression system,in accordance with an aspect of the present disclosure.

FIG. 4 is a schematic illustration of spectral modification forsliding-band compression system, in accordance with an aspect of thepresent disclosure.

FIG. 5 is a schematic illustration of efficient implementation of thesignal processing for noise suppression and dynamic range compression bysharing the operations of the FFT-based analysis-synthesis, inaccordance with an embodiment of the present disclosure.

FIG. 6 is a schematic illustration of the personal communication devicewith the hearing aid application for noise suppression and dynamic rangecompensation, in accordance with an embodiment of the presentdisclosure.

FIG. 7 is a screenshot of the home screen of the app, in accordance withan aspect of the present disclosure.

FIG. 8 is a screenshot of the settings screen. Panel (a) of the figureshows screenshot of the settings screen for noise suppression. Panel (b)of the figure shows screenshot of the settings screen for dynamic rangecompression.

FIG. 9 shows an example of processing for dynamic range compression.Panel (a) of the figure shows input signal of amplitude modulated toneof 1 kHz. Panel (b) of the figure shows GUI parameters set for constantgain of 12 dB and compression ratio of 2. Panel (c) of the figure showsthe processed output.

FIG. 10 shows an example of processing for dynamic range compression.Panel (a) of the figure shows a speech signal with large amplitudevariation. Panel (b) of the figure shows processed speech withparameters as shown in 9(b).

DETAILED DESCRIPTION OF THE INVENTION

The present disclosure discloses a method enabling use of a personalcommunication device, such as a smartphone with an operating system andat least one application embedded in the device for processing thestream of audio signals, as a hearing aid. The smartphone as hearing aidenables fine-tuning of the frequency-dependent parameters through aninteractive mode using a touch-controlled graphical user interface(GUI). The smartphone along with the GUI enables signal processing forsuppression of background noise and dynamic range compression tocompensate for the frequency-dependent increase in hearing thresholdsand decrease in the dynamic range associated with sensorineural hearingloss. The dynamic range compression may be carried out usingsliding-band compression to overcome the problems associated withconventionally used single-band compression and multiband compression.

Suppression of background noise is necessary to enhance the speechsignal for use in hearing aids. Single-channel speech enhancement isuseful in such applications, particularly when a second microphonecannot be used due to space or cost constraints.

Single-channel speech enhancement using spectral subtraction based onthe geometric approach may be used for suppression of background noise,as it results in smaller residual noise. FIG. 1 shows a block diagram ofthe spectral subtraction technique for suppressing additive noise. Theprocessing may comprise the processing blocks of windowing (101), FFTcalculation (102), magnitude spectrum calculation (103), noiseestimation (104), SNR-dependent gain calculation (105), enhanced complexspectrum calculation (106), IFFT calculation (107), re-synthesis usingoverlap-add (108), and delay (109). In this embodiment of theprocessing, the input speech x(m) (151) is windowed by the windowing 101to output analysis frames to the FFT calculation 152, which outputs thesignal complex spectrum X(n,k) (153), with n as the frame number and kas the frequency index. The magnitude spectrum calculation 103 uses thecomplex spectrum 153 as the input and outputs the signal magnitudespectrum |X(n,k)| (154), which is input to the noise estimation 104 toobtain the noise magnitude spectrum {circumflex over (D)}(n,k) (155).The SNR-dependent gain calculation 105 is used to obtain anSNR-dependent gain function G_(GA)(n,k) (156). The speech enhancement iscarried out by multiplying the SNR-dependent gain function 156 with thesignal complex spectrum 153 to obtain the enhanced complex spectrumY(n,k) (157). The SNR-dependent gain calculation (105) receives theenhanced complex spectrum 157 through the delay 109, noise magnitudespectrum 155, and the signal magnitude spectrum 154, and outputs theSNR-dependent gain 156. The res-synthesis using overlap-add 108 receivesthe output 159 of the IFFT calculation 107 and outputs the enhancedspeech y(m) (160).

The dynamic quantile tracking based noise estimation may be used alongwith spectral subtraction for noise suppression. For each frequencyindex of the spectrum, the most frequently occurring value, obtained asthe peak of the histogram, can be reported to be representative of thenoise value. The noise estimation method can dynamically estimatehistogram using the dynamic quantile tracking with low memory andcomputation requirements. The peak of the histogram may be used as theadaptive quantile for estimating the noise at each frequency index. Thehistogram can be estimated by dynamically tracking multiple quantilevalues for a set of evenly spaced probabilities. The desired quantilecorresponding to the peak of the histogram may be obtained by findingquantile for which the difference between neighboring quantile values isminimum.

In a preferred computationaly efficient embodiment, the estimate ofp_(i)(k)-quantile, {circumflex over (q)}_(i)(n,k), is obtained byapplying an increment or a decrement on its previous estimate as{circumflex over (q)} _(i)(n,k)={circumflex over (q)} _(i)(n−1,k)+d_(i)(n,k)  (1)The change d_(i)(n,k) is given as

$\begin{matrix}{{d_{i}\left( {n,k} \right)} = \left\{ \begin{matrix}{{\Delta_{i}^{+}(k)},} & \left| {X\left( {n,k} \right)} \middle| {\geq {{\hat{q}}_{i}\left( {{n - 1},k} \right)}} \right. \\{{- {\Delta_{i}^{-}(k)}},} & {otherwise}\end{matrix} \right.} & (2)\end{matrix}$where Δ_(i) ⁺(k) and Δ_(i) ⁻(k) are selected to be appropriate fractionsof the range R(n,k) asΔ_(i) ⁺(k)=λR(n,k)p _(i)(k)  (3)Δ_(i) ⁻(k)=λR(n,k)(1−p _(i)(k))  (4)The range is estimated using dynamic peak and valley detectors forupdating the peak P(n,k) and the valley V(n,k) using the followingequations:

$\begin{matrix}{{P\left( {n,k} \right)} = \left\{ \begin{matrix}\begin{matrix}{{\tau_{p}{P\left( {{n - 1},k} \right)}} +} \\{\left. \left( {1 - \tau_{p}} \right) \middle| {X\left( {n,k} \right)} \right|,}\end{matrix} & \left| {X\left( {n,k} \right)} \middle| {\geq {P\left( {{n - 1},k} \right)}} \right. \\\begin{matrix}{{\sigma_{p}{P\left( {{n - 1},k} \right)}} +} \\{{\left( {1 - \sigma_{p}} \right){V\left( {{n - 1},k} \right)}},}\end{matrix} & {otherwise}\end{matrix} \right.} & (5) \\{{V\left( {n,k} \right)} = \left\{ \begin{matrix}\begin{matrix}{{\tau_{v}{V\left( {{n - 1},k} \right)}} +} \\{\left. \left( {1 - \tau_{v}} \right) \middle| {X\left( {n,k} \right)} \right|,}\end{matrix} & \left| {X\left( {n,k} \right)} \middle| {\leq {V\left( {{n - 1},k} \right)}} \right. \\\begin{matrix}{{\sigma_{v}{V\left( {{n - 1},k} \right)}} +} \\{{\left( {1 - \sigma_{v}} \right){P\left( {{n - 1},k} \right)}},}\end{matrix} & {otherwise}\end{matrix} \right.} & (6) \\{{R\left( {n,k} \right)} = {{P\left( {n,k} \right)} - {V\left( {n,k} \right)}}} & (7)\end{matrix}$The dynamic quantile tracking to estimate quantile {circumflex over(q)}_(i)(n,k) as given by Equations 1, 2, and 7 can be written as thefollowing:

$\begin{matrix}{{{\hat{q}}_{i}\left( {n,k} \right)} = \left\{ \begin{matrix}\begin{matrix}{{{\hat{q}}_{i}\left( {{n - 1},k} \right)} +} \\{{{{\lambda p}_{i}(k)}{R\left( {n,k} \right)}},}\end{matrix} & \left| {X\left( {n,k} \right)} \middle| {\geq {{\hat{q}}_{i}\left( {{n - 1},k} \right)}} \right. \\\begin{matrix}{{{\hat{q}}_{i}\left( {{n - 1},k} \right)} -} \\{{\lambda\left( {1 - {p_{i}(k)}} \right){R\left( {n,k} \right)}},}\end{matrix} & {otherwise}\end{matrix} \right.} & (8)\end{matrix}$

A block diagram of the computation steps as given in Equations 1-8 isshown in FIG. 2, with two main processing blocks (marked by dottedoutlines) of range estimation 201 and quantile estimation 202. The rangeestimation block (201) receives the input magnitude spectral sample|X_(n)(k)| (154) as the input and outputs the estimated range of thenoise spectral sample R_(n)(k) (251). The quantile estimation block 202receives |X_(n)(k)| (154) and R_(n)(k) (251) as the inputs and outputsthe quantile estimate {circumflex over (q)}_(i)(n,k) (255). In the rangeestimation block 201, the peak calculator 211 calculates the peakP_(n)(k) (252) using Equation 5 and the output 253 of the delay 212. Thevalley calculator 213 calculates the valley V_(n)(k) 254 using Equation6 and the output 255 of the delay 214. The range R_(n)(k) (251) iscalculated by the difference block 215 using Equation 7. In the quantileestimation block 202, the quantile calculator 216 calculates {circumflexover (q)}_(i)(n,k) (255) using Equation 8 and the output 256 of thedelay 217. The quantile {circumflex over (q)}_(i)(n,k) (255) is used tocalculate the noise magnitude spectrum {circumflex over (D)}(n,k) (155),as

$\begin{matrix}{{{{\hat{D}\left( {n,k} \right)} = {{argmin}_{{\hat{q}}_{i}{({n,k})}}\left\lbrack {{{\hat{q}}_{i}\left( {n,k} \right)} - {{\hat{q}}_{i - 1}\left( {n,k} \right)}} \right\rbrack}};{i = 2}},3,\ldots,J} & (9)\end{matrix}$where J is the number of quantiles tracked.

The processing for sliding-band compression may be carried out as shownin FIG. 3, comprising the cascaded processing blocks of short-timespectral analysis 301, spectral modification 302, and re-synthesis usingoverlap-add 303. To compensate for increased hearing thresholds andreduced dynamic range, a frequency-dependent gain function can becalculated in accordance with the desired levels for ‘soft’,‘comfortable’, and ‘loud’ sounds (referred to as SL, CL, LL,respectively). For each frequency index k, the spectral modification canbe carried out using a piecewise linear relationship between the inputpower and the output power on dB scale. The relationship is specified bythe values of P_(OdBSL)(k), P_(OdCL)(k), and P_(OdBLL)(k) which are theoutput signal levels corresponding to soft, comfortable, and loudsounds, respectively, for the hearing aid user and by the values ofP_(IdBSl)(k) and P_(IdBLL)(k) which are the input signal levelscorresponding to soft and loud sounds, respectively, for anormal-hearing listener. The relationship can be defined in threeregions with the compression ratio as ‘CR=1’, ‘CR>1’, and ‘CR==∞’ in thefirst, second, and third region respectively. With G_(IdB)(k)=P_(OdBSL)(k)−P_(IdBSL)(k), the target gain for the frequency index kin the nth frame in the three regions is given as

$\begin{matrix}{{G_{TdB}\left( {n,k} \right)} = \left\{ \begin{matrix}{{G_{LdB}(k)},} & \begin{matrix}{{P_{IdB}\left( {n,k} \right)} <} \\{{P_{OdBCL}(k)} -} \\{G_{LdB}(k)}\end{matrix} \\{\frac{\begin{matrix}{{G_{LdB}(k)} -} \\\left\{ {{P_{IdB}\left( {n,k} \right)} -} \right. \\{\left. {P_{OdBCL}(k)} \right\}\left\{ {{{CR}(k)} - 1} \right\}}\end{matrix}}{{CR}(k)},} & \; \\\begin{matrix}{{P_{OdBCL}(k)} -} \\{{G_{LdB}(k)} \leq} \\{{P_{IdB}\left( {n,k} \right)} \leq} \\{P_{IdBLL}(k)}\end{matrix} & \; \\\begin{matrix}{{P_{OdBLL}(k)} -} \\{{P_{IdB}\left( {n,k} \right)},}\end{matrix} & \begin{matrix}{{P_{IdB}\left( {n,k} \right)} >} \\{P_{IdBLL}(k)}\end{matrix}\end{matrix} \right.} & (10)\end{matrix}$

A block diagram of the processing for spectral modification forsliding-band compression is shown in FIG. 4, with the input short-timecomplex spectrum 352 as the input and the modified short-time complexspectrum 353 as the output. For the frequency index k in the nth frame,the input level P_(IdB)(n,k) is calculated, from the input short-timecomplex spectrum 352, as the sum of squared magnitudes of the spectralsamples in the band centered at k and with the bandwidth correspondingto the auditory critical bandwidth

$\begin{matrix}{{{BW}(k)} = {25 + {75\left( {1 + {1.4\left( {f(k)} \right)^{2}}} \right)^{0.69}}}} & (11)\end{matrix}$where f(k) is the frequency, in kHz, corresponding to the kth spectralsample. For spectral modification, the target gain is converted to alinear scale. The gain 454 applied for the frequency index k in the nthframe is obtained using the desired attack and release rates by updatingthe gain from the previous value towards the target 453, as given inEquation 10. It is given as

$\begin{matrix}{{G\left( {i,k} \right)} = \left\{ \begin{matrix}{{{\max\left( {{{G\left( {{n - 1},k} \right)}/\gamma_{a}},{G_{T}\left( {n,k} \right)}} \right)}.{G_{T}\left( {n,k} \right)}} <} \\{G\left( {{n - 1},k} \right)} \\{{\min\left( {{{G\left( {{n - 1},k} \right)}\gamma_{r}},{G_{T}\left( {n,k} \right)}} \right)} \geq {G\left( {{n - 1},k} \right)}}\end{matrix} \right.} & (12)\end{matrix}$The number of steps during the attack and release phases may becontrolled using gain ratios γ_(α)=(G_(max)/G_(min))^(1/s) ^(α) andγ_(r)=(G_(max)/G_(min))^(1/s) ^(r) , respectively. Here G_(max) andG_(min) are the maximum and minimum possible values of the target gain.The number of steps s_(α) during attack and the number of steps s_(r)during release are selected to set the attack time asT_(a)=S_(a)S_(α)S/f_(s) and the release time as T_(r)=S_(r)S/f_(s),where f_(s)=sampling frequency and S=number of samples for frameshift. Afast attack avoids the output level from exceeding the uncomfortablelevel during transients, and a slow release avoids amplification ofbreathing.

An efficient implementation of the processing by sharing thecomputation-intensive operations of the FFT-based analysis-synthesis fornoise suppression based on computationally efficient dynamic quantiletracking and sliding-band dynamic range compression may be carried outas shown in FIG. 5, with the digitized speech input 552 as the inputsignal and the digitized speech output 558 as the output signal. Theimplementation may comprise the processing blocks for windowing withoverlap 502, FFT calculation 503, noise suppression 504, dynamic rangecompression 505, IFFT calculation 506, windowing with overlap 507, andGUI for parameter setting 509.

FIG. 6 shows a block diagram of an exemplary implementation of thehearing aid app. The setup comprises a personal communication devicehandset 602, such as a smartphone handset and a headset 601. The headset601 comprises a microphone and a pair of earphones with associated wiresand switching. The handset 602 further comprises a codec 612, aprocessor 603, and a display with touch screen (not shown in the figure)for the user interface. The input signal acquired from the microphone604 of the headset 601 is amplified and converted to digital samples bythe analog-to-digital converter (ADC) 607 of the codec 612. Thesesamples may be buffered, processed, and buffered by the processor. Theresulting samples can be output through the digital-to-analog converter(DAC) 610 of the codec 612 and amplified. The resulting signal is outputthrough the earphones 605 of the headset. In an exemplary embodiment ofthe processing, the analysis-synthesis may be carried out using 20-msframes with 75% frame overlap and 1024-point FFT, with the processingparameters selected as sampling frequency=24 kHz, L=480, S=120, andN=1024.

A screenshot of the home screen of the app, in accordance with an aspectof the present disclosure is illustrated in FIG. 7. The play/stop buttonis for control of the output. All processing modules have individual‘on/off’ and ‘settings’ buttons, with the on/off button for toggling theprocessing and the settings button for setting the processing parametersgraphically. FIG. 8a shows an exemplary screenshot of the ‘settings’screen for noise suppression and dynamic range compression modules.Settings for noise suppression module provides a user interface withtouch control of points, called ‘thumbs’, for selecting the values ofover-subtraction factor α as function of frequency. The values of α canbe set as 1-5 for up to 10 frequencies and the values for all theintermediate frequencies are obtained by smooth curve fitting. Thescreenshot of the ‘settings’ screen for dynamic range compressionshowing graphical controls for the SL, CL, and LL values, is shown inFIG. 8b . The user interface consists of three touch-controlled curvesto set the values of SL, CL, and LL across frequencies. Control pointscalled as thumbs are provided to adjust the curves. Each curve mayconsist of a number of thumbs. Provision may be made to store andretrieve multiple parameter settings. The user interface may also have‘undo’ and ‘redo’ buttons to access recent thumb movements. Such animplementation enables the user to adjust the processing parameters inan interactive and real-time mode, to save the processing parameters asone of the profiles, or to select the most appropriate profile from thesaved ones.

An example of the dynamic range compression with an amplitude modulatedinput is shown in FIG. 9. Input is an amplitude-modulated tone of 1 kHzand processing parameters are set as shown in FIG. 9(b) with acompression ratio of 2. The processing gives higher gains at lowervalues of the input level. Spikes in the amplitude envelope of theoutput signal in response to step changes in the amplitude envelope ofthe input signal, as seen in the figure, are typical of the dynamicrange compression with a finite frameshift and can be eliminated byusing one-sample frameshift but with a significantly increasedcomputation load. Another example of the processing is shown in FIG. 10,for an amplitude modulated concatenation of speech signals. The inputconsists of three isolated vowels, a Hindi sentence, and an Englishsentence, (-/a/-/i/-/u/-“aaiye aap ka naam kya hai?”—“where were you ayear ago?”). For different speech materials, music, and environmentalsounds with large variation in the sound level as the input, the outputexhibited the desired amplification and compression without introducingperceptible distortions.

In an embodiment of the invention to enable the use of a smartphone as ahearing aid, integration of the signal processing for dynamic quantiletracking based noise suppression and sliding-band dynamic rangecompression has been implemented using ‘LG Nexus 5X’ running ‘Android7.1’. The processing parameters can be set by the user in an interactiveand real-time mode using a graphical touch interface. The audio latencyof the implementation was 45 ms, which is much less than thedetectability threshold of 125 ms for audio-visual delay, and hence maybe considered as acceptable for a hearing aid during face-to-faceconversation.

The foregoing description of the invention is to be considered asexemplary and not restrictive, as the processing blocks described in thedisclosure may be partitioned and/or combined in many ways and the appcan be implemented using other smartphones and other types of graphicaluser interfaces.

We claim:
 1. A method for real-time signal processing to process adigitized input speech signal, by a personal communication devicecomprising an input amplifier, an analog-to-digital converter, adigital-to-analog converter, an output amplifier, a digital processor,and a graphical user interface, using Fast Fourier Transform (FFT-based)analysis-synthesis for a quantile-based noise suppression to reducebackground noise and a sliding-band dynamic range compression tocompensate for frequency-dependent hearing loss and reduced dynamicrange, in order to improve speech quality and intelligibility forhearing-impaired listeners, wherein the processing for thequantile-based noise suppression comprises the steps of: (i) performinga dynamic quantile tracking based noise estimation by estimating ahistogram for each frequency index of spectrum of the digitized inputspeech signal, wherein the histogram is estimated by dynamicallytracking a plurality of quantile values for frequency indices of thespectrum, obtaining a quantile value corresponding to peak of thehistogram by finding a quantile value having a minimum differencebetween neighboring quantile values, and estimating noise at eachfrequency index by using peak of the histogram, wherein the quantilevalue is calculated by applying an increment or a decrement on itsprevious value, with the increment and decrement selected to be afraction of a dynamically estimated range of the frequency index suchthat the quantile value approaches a sample quantile over a number ofsuccessive analysis frames; and (ii) performing spectral subtraction ofthe noise at each frequency index from the spectrum to obtain enhancedcomplex spectrum; and the processing for the sliding-band dynamic rangecompression is carried out by calculating a frequency-dependent gainfunction in accordance with a level in a band centered at each frequencyindex and a piecewise linear relation between an input power and anoutput power on a dB scale for each spectral sample and the gainfunction is used for modification of the spectrum, and wherein thegraphical user interface provides the plurality of control settings tofacilitate setting and fine tuning of frequency-dependent parameters forthe signal processing in an interactive and real-time mode, wherein thegraphical user interface provides a plurality of control settings to theuser for setting parameters of the dynamic quantile tracking based noisesuppression and the sliding-band dynamic range compression process. 2.The real-time signal processing method as claimed in claim 1, whereinthe FFT-based analysis-synthesis is carried out with 20-ms frames with75% frame overlap.
 3. A digital hearing aid system, comprising: aheadset with a microphone acquiring an analog input speech signal and anearphone for outputting a processed analog speech output signal; apersonal communication device in communication with the headset, whereinthe personal communication device comprises: an input amplifieramplifying the analog input speech signal acquired by the microphone ofthe headset; an analog-to-digital converter to convert an amplifiedanalog input speech signal from the input amplifier to a digitized inputspeech signal; a digital-to-analog converter to convert a processeddigital speech output signal to an analog output signal; an outputamplifier amplifying the analog output signal and outputting theprocessed analog speech output signal to the earphone of the headset; adigital processor interfaced to the analog-to-digital converter and thedigital-to-analog converter; and a graphical user interface incommunication with the digital processor; wherein the digital processorof the personal communication device is configured to process thedigitized input speech signal using Fast Fourier Transform (FFT-based)analysis-synthesis for a quantile-based noise suppression to reducebackground noise and a sliding-band dynamic range compression tocompensate for frequency-dependent hearing loss and reduced dynamicrange, in order to improve speech quality and intelligibility forhearing-impaired listeners, wherein the processing for thequantile-based noise suppression comprises the steps of: (i) performinga dynamic quantile tracking based noise estimation by estimating ahistogram for each frequency index of spectrum of the digitized inputspeech signal, wherein the histogram is estimated by dynamicallytracking a plurality of quantile values for frequency indices of thespectrum, obtaining a quantile value corresponding to peak of thehistogram by finding a quantile value having a minimum differencebetween neighboring quantile values, and estimating noise at eachfrequency index by using peak of the histogram wherein the quantilevalue is calculated by applying an increment or a decrement on itsprevious value, with the increment and decrement selected to be afraction of a dynamically estimated range of frequency index such thatthe quantile value approaches a sample quantile over a number ofsuccessive analysis frames; and (ii) performing spectral subtraction ofthe noise at each frequency index from the spectrum to obtain enhancedcomplex spectrum; and the processing for the sliding-band dynamic rangecompression is carried out by calculating a frequency-dependent gainfunction in accordance with a level in a band centered at each frequencyindex and a piecewise linear relation between an input power and anoutput power on a dB scale for each spectral sample and the gainfunction is used for modification of the spectrum, and wherein thegraphical user interface provides a plurality of control settings tofacilitate setting and fine tuning of frequency-dependent parameters forthe signal processing in an interactive and real-time mode, wherein thegraphical user interface provides a plurality of control settings to theuser for setting parameters of the dynamic quantile tracking based noisesuppression and the sliding-band dynamic range compression process. 4.The digital hearing aid system as claimed in claim 3, wherein thegraphical user interface is a touch screen interface.